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| import gradio as gr | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM | |
| TITLE = "Bloom’s Taxonomy Helper (Classify + Generate + Rewrite)" | |
| # ------------------ Bloom labels ------------------ | |
| LABELS = ["Remember", "Understand", "Apply", "Analyze", "Evaluate", "Create"] | |
| # ------------------ Generation templates by Bloom ------------------ | |
| PROMPT_TEMPLATES = { | |
| "Remember": ( | |
| "Write {n} distinct factual recall questions for college students.\n" | |
| "Topic: {topic}\n" | |
| "Cognitive focus: Remember (recognize/recall facts and terms).\n" | |
| "Style: Use 'What', 'When', 'Define', or 'List'.\n" | |
| "Do not include answers. Number each on its own line." | |
| ), | |
| "Understand": ( | |
| "Write {n} distinct comprehension questions for college students.\n" | |
| "Topic: {topic}\n" | |
| "Cognitive focus: Understand (explain, summarize, interpret).\n" | |
| "Style: Use 'Explain', 'Summarize', 'Describe', or 'Give an example of'.\n" | |
| "Do not include answers. Number each on its own line." | |
| ), | |
| "Apply": ( | |
| "Write {n} distinct application questions for college students.\n" | |
| "Topic: {topic}\n" | |
| "Cognitive focus: Apply (use procedures, compute, demonstrate use).\n" | |
| "Style: Ask students to calculate, implement, or solve with concrete data.\n" | |
| "Avoid 'define' or 'explain' prompts. Do not include answers. Number each on its own line." | |
| ), | |
| "Analyze": ( | |
| "Write {n} distinct analysis questions for college students.\n" | |
| "Topic: {topic}\n" | |
| "Cognitive focus: Analyze (compare/contrast, break down relationships, cause–effect).\n" | |
| "Style: Use 'Compare', 'Differentiate', 'Explain why', 'Break down', or 'Trace'.\n" | |
| "Do not include answers. Number each on its own line." | |
| ), | |
| "Evaluate": ( | |
| "Write {n} distinct evaluation questions for college students.\n" | |
| "Topic: {topic}\n" | |
| "Cognitive focus: Evaluate (judge, critique, justify with criteria and evidence).\n" | |
| "Style: Use 'Argue', 'Defend', 'Critique', 'Which is better and why', or 'Assess'.\n" | |
| "Do not include answers. Number each on its own line." | |
| ), | |
| "Create": ( | |
| "Write {n} distinct creation/synthesis tasks for college students.\n" | |
| "Topic: {topic}\n" | |
| "Cognitive focus: Create (design, plan, invent, propose, produce a novel artifact).\n" | |
| "Style: Use 'Design', 'Propose', 'Develop', 'Compose', or 'Build'. Include realistic constraints.\n" | |
| "Do not include answers. Number each on its own line." | |
| ), | |
| } | |
| # ------------------ Pipelines ------------------ | |
| # Zero-shot classifier (no training) | |
| clf = pipeline("zero-shot-classification", model="typeform/distilbert-base-uncased-mnli") | |
| # Higher-quality generator (CPU-friendly but better than *small*) | |
| GEN_MODEL = "google/flan-t5-base" | |
| gen_tok = AutoTokenizer.from_pretrained(GEN_MODEL) | |
| gen_mdl = AutoModelForSeq2SeqLM.from_pretrained(GEN_MODEL) | |
| gen = pipeline("text2text-generation", model=gen_mdl, tokenizer=gen_tok) | |
| # ------------------ Helpers ------------------ | |
| def clean_numbering(text: str, n: int) -> str: | |
| """Force clean 1..n numbering, each on its own line.""" | |
| lines = [ln.strip(" -*\t") for ln in text.splitlines() if ln.strip()] | |
| # If model returned a paragraph, split by sentence end | |
| if len(lines) == 1 and "1." not in lines[0]: | |
| import re | |
| parts = [p.strip() for p in re.split(r"(?<=[.?!])\s+", lines[0]) if p.strip()] | |
| lines = parts | |
| lines = [ln for ln in lines if any(c.isalpha() for c in ln)] | |
| lines = lines[:max(1, n)] | |
| return "\n".join(f"{i+1}. {ln.lstrip('0123456789. ').strip()}" for i, ln in enumerate(lines)) | |
| # ------------------ Functions ------------------ | |
| def classify_bloom(question: str): | |
| question = (question or "").strip() | |
| if not question: | |
| return "", "" | |
| res = clf(question, LABELS) | |
| labels = res["labels"] | |
| scores = [round(float(s), 3) for s in res["scores"]] | |
| top = labels[0] if labels else "" | |
| table = "\n".join(f"{l}: {s}" for l, s in zip(labels, scores)) | |
| return top, table | |
| def generate_questions(topic: str, level: str, n: int, creativity: float): | |
| topic = (topic or "").strip() | |
| if not topic: | |
| return "Please enter a topic." | |
| template = PROMPT_TEMPLATES.get(level, PROMPT_TEMPLATES["Understand"]) | |
| prompt = template.format(n=int(n), topic=topic) | |
| out = gen( | |
| prompt, | |
| max_new_tokens=180, | |
| do_sample=(creativity > 0.01), | |
| temperature=max(0.01, min(1.2, creativity)), | |
| top_p=0.9, | |
| num_beams=1, | |
| )[0]["generated_text"] | |
| return clean_numbering(out, int(n)) | |
| def rewrite_level(question: str, target_level: str): | |
| question = (question or "").strip() | |
| if not question: | |
| return "Paste a question to rewrite." | |
| # Leverage the template for target level to steer rewriting | |
| template = PROMPT_TEMPLATES.get(target_level, PROMPT_TEMPLATES["Understand"]) | |
| prompt = ( | |
| f"{template}\n\n" | |
| f"Transform the following single question to match the level above. Keep it concise and do not include the answer.\n" | |
| f"Original: {question}\n" | |
| f"Return exactly 1 numbered question." | |
| ) | |
| out = gen(prompt, max_new_tokens=100, do_sample=False)[0]["generated_text"] | |
| return clean_numbering(out, 1) | |
| # ------------------ UI ------------------ | |
| with gr.Blocks(title=TITLE) as demo: | |
| gr.Markdown(f"# {TITLE}") | |
| gr.Markdown( | |
| "Classify questions by Bloom level, generate new questions aligned to a level, " | |
| "and rewrite a question to a different level. Runs fully on open models." | |
| ) | |
| with gr.Tab("Classify"): | |
| q = gr.Textbox( | |
| label="Enter a question", | |
| lines=4, | |
| placeholder="e.g., Explain why randomized controlled trials reduce bias." | |
| ) | |
| top = gr.Textbox(label="Predicted Bloom level", interactive=False) | |
| scores = gr.Textbox(label="All scores", interactive=False) | |
| gr.Button("Classify").click(classify_bloom, [q], [top, scores]) | |
| with gr.Tab("Generate"): | |
| with gr.Row(): | |
| topic = gr.Textbox(label="Topic", value="binary numbers in computer science") | |
| level = gr.Dropdown(LABELS, value="Apply", label="Bloom level") | |
| with gr.Row(): | |
| n = gr.Slider(1, 10, value=5, step=1, label="How many questions") | |
| creativity = gr.Slider(0.0, 1.2, value=0.6, step=0.1, label="Creativity (temperature)") | |
| out = gr.Textbox(label="Generated questions", lines=12) | |
| gr.Button("Generate").click(generate_questions, [topic, level, n, creativity], out) | |
| with gr.Tab("Rewrite"): | |
| q2 = gr.Textbox(label="Original question", lines=4, value="Define binary number.") | |
| target = gr.Dropdown(LABELS, value="Analyze", label="Target Bloom level") | |
| out2 = gr.Textbox(label="Rewritten question", lines=4) | |
| gr.Button("Rewrite").click(rewrite_level, [q2, target], out2) | |
| demo.launch() |